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import json
import pandas as pd
import csv
from typing import Dict, Union
import locale
model_details = {
"DeepSeek R1": ("https://huggingface.co/deepseek-ai/DeepSeek-R1", 685, "General"),
"Llama 3.1 405B": ("https://huggingface.co/meta-llama/Llama-3.1-405B", 406, "General"),
"Llama 3.(1-3) 70B": ("https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct", 70.6, "General"),
"Qwen2.5 72B": ("https://huggingface.co/Qwen/Qwen2.5-72B-Instruct", 72.7, "General"),
"Qwen2.5 32B": ("https://huggingface.co/Qwen/Qwen2.5-32B", 32.5, "General"),
"StarChat2 15B v0.1": ("https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1", 16, "General"),
"DeepSeek R1 Distill Qwen 14B": ("https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", 14.8, "General"),
"CodeLlama 70B": ("https://huggingface.co/codellama/CodeLlama-70b-hf", 69, "Coding"),
"QwenCoder 2.5 32B": ("https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct", 32.5, "Coding"),
"DeepSeek Coder 33B": ("https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct", 33.3, "Coding"),
"QwenCoder 2.5 14B": ("https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct", 14.7, "Coding"),
"OpenCoder 8B": ("https://huggingface.co/infly/OpenCoder-8B-Instruct", 7.77, "Coding"),
"QwenCoder 2.5 7B": ("https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct", 7.61, "Coding"),
"DeepSeek Coder 6,7B": ("https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct", 6.74, "Coding"),
"HaVen-CodeQwen": ("https://huggingface.co/yangyiyao/HaVen-CodeQwen", 7.25, "RTL-Specific"),
"CodeV-CL-7B": ("https://huggingface.co/yang-z/CodeV-CL-7B", 6.74, "RTL-Specific"),
"CodeV-QW-7B": ("https://huggingface.co/yang-z/CodeV-QW-7B", 7.25, "RTL-Specific"),
"CodeV-DS-6.7B": ("https://huggingface.co/yang-z/CodeV-DS-6.7B", 6.74, "RTL-Specific"),
"RTLCoder Mistral": ("https://huggingface.co/ishorn5/RTLCoder-v1.1", 7.24, "RTL-Specific"),
"RTLCoder DeepSeek": ("https://huggingface.co/ishorn5/RTLCoder-Deepseek-v1.1", 6.74, "RTL-Specific"),
"OriGen": ("https://huggingface.co/henryen/OriGen_Fix", 6.74, "RTL-Specific")
}
def get_headers(reader) -> Union[list, list]:
metrics, benchs = [], []
for i, row in enumerate(reader):
if i == 0:
metrics = row[1:]
elif i == 1:
benchs = row[1:]
break
return metrics, benchs
def get_model_params_and_url(model) -> Union[str, str, float]:
if model not in model_details:
return "-", "-", "-"
url = model_details[model][0]
params = model_details[model][1]
type = model_details[model][2]
return url, params, type
def parse_results(csv_path: str) -> list[dict]:
"""
Each row has the following format:
MODEL | BENCHMARK | TASK | METRIC | RESULT
"""
dataset = []
models = []
with open(csv_path, newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
metrics, benchs = get_headers(reader)
for i, row in enumerate(reader):
model = row[0]
url, params, type = get_model_params_and_url(model)
models.append(model)
row = row[1:]
ctr = 0
for metric, bench in zip(metrics, benchs):
if metric == "EM":
metric = "Exact Matching (EM)"
record = {}
record["Model"] = model
record["Model Type"] = type
record["Benchmark"] = bench
record["Task"] = metric
record["Result"] = float(row[ctr].replace(',','.'))
record["Model URL"] = url
record["Params"] = params
dataset.append(record)
ctr += 1
print(models)
return dataset
def writeJson(data: list):
with open('results.json', 'w') as f:
json.dump(data, f, indent=4, ensure_ascii=False)
print("Done")
def read_json():
json_path = "./results.json"
with open(json_path, "r", encoding="utf-8") as file:
data = json.load(file)
return data
def read_data() -> Union[pd.DataFrame, list, list, str]:
data = read_json()
df = pd.DataFrame(data)
df.rename(columns={'Model': 'Model', 'Benchmark': 'Benchmark', 'Task': 'Metric', 'Result': 'Score', 'EM': 'Exact Matching (EM)'}, inplace=True)
df['Params'] = pd.to_numeric(df['Params'], errors='coerce')
benchmarks = sorted(df['Benchmark'].unique().tolist(), reverse=True)
metrics = df['Metric'].unique().tolist()
default_metric = 'Functionality (FNC)' if 'Functionality (FNC)' in metrics else metrics[0]
return df, benchmarks, metrics, default_metric
if __name__ == "__main__":
csv_path = "./results.csv"
d = parse_results(csv_path)
writeJson(d)
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